MétaCan
Menu
Back to cohort
Record W2002027782 · doi:10.1109/ism.2012.95

Thin and Light Video Editing Extensions for Education with Opencast Matterhorn

2012· article· en· W2002027782 on OpenAlex
Greg Logan, Jim Greer, Gord McCalla

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Analysis and Summarization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFrench hornClipping (morphology)Computer sciencePlan (archaeology)SoftwareVideo editingWeb applicationMultimediaState (computer science)Human–computer interactionWorld Wide WebOperating systemGeologyProgramming languageAcoustics

Abstract

fetched live from OpenAlex

This paper presents the current state of our research project which aims to give users a simple, easy to use, and computationally light way of creating mashups of lecture content within the Opencast Matter horn lecture capture system. The system modifies the playback components of Matter horn to deliver thin and light video clipping functionality without requiring installation of any additional software. We plan to make use of the extensive logging framework built into Matter horn to examine the effects of this tool on learner engagement.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.242
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations2
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicVideo Analysis and SummarizationFrench-language works237,207